59 citations · 110 across the 5 of their papers we have counts for
5 papers · 1 filter
A Benchmark of Dynamical Variational Autoencoders applied to Speech Spectrogram Modeling
Xiaoyu Bie, Laurent Girin, Simon Leglaive +2
The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space lea…
Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders
Mostafa Sadeghi, Simon Leglaive, Xavier Alameda-PIneda +2
Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data. VAEs have been successfully used to lear…
Speech enhancement with variational autoencoders and alpha-stable distributions
Simon Leglaive, Umut Simsekli, Antoine Liutkus +2
This paper focuses on single-channel semi-supervised speech enhancement. We learn a speaker-independent deep generative speech model using the framework of variational autoencoders…
A variance modeling framework based on variational autoencoders for speech enhancement
Simon Leglaive, Laurent Girin, Radu Horaud
In this paper we address the problem of enhancing speech signals in noisy mixtures using a source separation approach. We explore the use of neural networks as an alternative to a…
Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization
Simon Leglaive, Laurent Girin, Radu Horaud
In this paper we address speaker-independent multichannel speech enhancement in unknown noisy environments. Our work is based on a well-established multichannel local Gaussian mode…